1. Field of the Invention
The present invention relates, generally to the recognition of a physical presence in a seat and, more specifically, to a method of recognizing and classifying the occupancy of a vehicle seat having an occupancy sensing system.
2. Description of the Related Art
Automotive vehicles employ seating systems that accommodate the passengers of the vehicle. The seating systems include restraint systems that are calculated to restrain and protect the occupants in the event of a collision. The primary restraint system commonly employed in most vehicles today is the seatbelt. Seatbelts usually include a lap belt and a shoulder belt that extends diagonally across the occupant's torso from one end of the lap belt to a mounting structure located proximate to the occupant's opposite shoulder.
In addition, automotive vehicles may include supplemental restraint systems. The most common supplemental restraint system employed in automotive vehicles today is the inflatable airbag. In the event of a collision, the airbags are deployed as an additional means of restraining and protecting the occupants of the vehicle. Originally, the supplemental inflatable restraints (airbags) were deployed in the event of a collision whether or not any given seat was occupied. These supplemental inflatable restraints and their associated deployment systems are expensive and over time this deployment strategy was deemed not to be cost effective. Thus, there became a recognized need in the art for a means to selectively control the deployment of the airbags such that deployment occurs only when the seat is occupied.
Partially in response to this need, vehicle safety systems have been proposed that include vehicle occupant sensing systems capable of detecting whether or not a given seat is occupied. The systems act as a switch in controlling the deployment of a corresponding air bag. As such, if the occupant sensing device detects that a seat is unoccupied during a collision, it can prevent the corresponding air bag from deploying, thereby saving the vehicle owner the unnecessary cost of replacing the expended air bag.
Furthermore, many airbag deployment forces and speeds have generally been optimized to restrain one hundred eighty pound males because the one hundred eighty pound male represents the mean average for all types of vehicle occupants. However, the airbag deployment force and speed required to restrain a one hundred eighty pound male exceeds that which are required to restrain smaller occupants, such as some females and small children. Thus, there became a recognized need in the art for occupant sensing systems that could be used to selectively control the deployment of the airbags when a person below a predetermined weight occupies the seat.
Accordingly, other vehicle safety systems have been proposed that are capable of detecting the weight of an occupant. In one such air bag system, if the occupant's weight falls below a predetermined level, then the system can suppress the inflation of the air bag or will prevent the air bag from deploying at all. This reduces the risk of injury that the inflating air bag could otherwise cause to the smaller-sized occupant.
Also, many airbag deployment forces and speeds have generally been optimized to restrain a person sitting generally upright towards the back of the seat. However, the airbag deployment force and speed may inappropriately restrain a person sitting otherwise. Thus, there became a recognized need in the art for a way to selectively control the deployment of an airbag depending on the occupant's sitting position.
Partially in response to this need, other vehicle safety systems have been proposed that are capable of detecting the position of an occupant within a seat. For example, if the system detects that the occupant is positioned toward the front of the seat, the system will suppress the inflation of the air bag or will prevent the air bag from deploying at all. This reduces the risk of injury that the inflating air bag could otherwise cause to the occupant. It can be appreciated that these occupant sensing systems provide valuable data, allowing the vehicle safety systems to function more effectively to reduce injuries to vehicle occupants.
One necessary component of each of the known systems discussed above includes some means for sensing the presence of the vehicle occupant in the seat. One such means may include a sensor device supported within the lower seat cushion of the vehicle seat. For example, U.S. published patent application having U.S. Ser. No. 10/249,527 and Publication No. US2003/0196495 A1 filed in the name of Saunders et al. discloses a method and apparatus for sensing seat occupancy including a sensor/emitter pair that is supported within a preassembled one-piece cylinder-shaped housing. The housing is adapted to be mounted within a hole formed in the seat cushion and extending from the B-surface toward the A-surface of the seat cushion. The sensor/emitter pair supported in the housing includes an emitter that is mounted within the seat cushion and spaced below the upper or A-surface of the seat cushion. In addition, the sensor is also supported by the housing within the seat cushion but spaced below the emitter. The cylindrical housing is formed of a compressible, rubber-like material that is responsive to loads placed on the upper surface of the seat cushion. The housing compresses in response to a load on the seat cushion. The load is detected through movement of the emitter toward the sensor as the housing is compressed. The housing is sufficiently resilient to restore the emitter to full height when no load is applied to the upper surface of the seat cushion. The Saunders et al. system also includes a processor for receiving the sensor signals and interpreting the signals to produce an output to indicate the presence of an occupant in the seat.
The sensors are arranged into a grid, or an array so that the sensors are collectively used to provide the raw input data as a depression or deflection pattern in the seat cushion. In this manner, systems of the type known in the related art take the data from the sensor array and process it, by a number of different means, in an attempt to determine the physical presence in the seat. A number of the prior art systems sense the defection of portions of the vehicle seat and attempt to discern from the sensor array data a recognized pattern that corresponds to one of the specified occupant classifications. To accomplish the pattern recognition, the best of these newer systems take the data derived from the sensed seat occupancy and process it through an artificial neural network (ANN). ANNs are more commonly referred to as neural networks, or simply, neural nets.
In general terms, a NN is essentially an interconnected assembly of simple processing element units, or nodes. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns. The NN may simply have an input and an output layer of units, or have an additional “hidden” layer or layers of units that internally direct the interconnection processes. The benefit to employing a NN approach is that, if properly trained, the NN will be able to generalize and infer the correct output responses from limited input data. Specifically in the case at hand, the NN based occupancy sensing systems determine that a physical presence is in a vehicle seat, recognize the type of physical presence by the sensor pattern it presents and pass this information to a restraint system control to determine if the pattern classification requires deployment or suppression of the airbag or other restraints.
Since a wide variety of individuals and objects (baby seats, for example) may be occupying a vehicle seat in a variety of seating positions, it is necessary to sort through a myriad of sensor array pattern inputs. However, for purposes of providing control inputs to a supplemental restrain system these large numbers of inputs from the sensor array are classifiable into a relatively small number of categories or classifications. When a NN to employed as a classification device for the variety of possible inputs, the NN must first be trained to understand the data it will receive. This is known as “supervised” learning, where the NN is provided both an input and the desired result. Supervised learning may be applied to a number of different known types of NNs, but the current methods used for classifying vehicle seat occupancy use one of the most common methodologies of “error back-propagation”.
In error back-propagation, which is known more simply as back-propagation (BP), the NN employs a “learning” rule whereby the weights of the unit connections are adjusted on the basis of the training data. The learning rule is essentially the algorithm used in the BP NN that will be the basis for deciding on how to classifying the actual incoming data, once the training is completed and the NN is put into use. During the training of a BP NN, the difference between the desired result and the actual output result of the NN for the given input provides an error that is used to adjust the connection weights. Changing the weights of the connections brings the NN results closer to the target result. The process of “back propagating” the determined “error” to adjust the connection weights gives this methodology its name. After training, the BP NN is tested, or validated by giving it only input values and seeing how close it comes to outputting the correct target values. The training may be continued if the validation of the BP NN does not give the desired results.
While the use of back propagation in a NN is relatively well established and it is one of the most commonly employed NN methodologies, it has distinct disadvantages when used in a NN for pattern recognition and classification. Back propagation causes the NN to learn specific target results rather than grouping the results into clusters or classifications. While this is useful and provides flexibility in many different applications, using a BP NN for pattern recognition and classification causes the BP NN to be confused and give non-sequitur results when attempting to classify a wide variety of possible inputs into relatively few target clusters of results, or classifications. In other words, the BP NN can be almost unbounded in its establishment of the number of possible results for the input data it processes, and even if it is limited to a specific number of outputs, it is unable to group a wide variety of somewhat similar results into clusters that are define as a class. Thus, when a BP NN based system is used for pattern recognition and classification, a series of additional steps are required to redefine all the specific targets results into the desired pattern recognition classifications. A number of the current occupant classification systems employ extensive filtering and reforming of the data in and out of a NN to achieve better results with a BP NN. This is inefficient and introduces errors that cannot be compensated for. Secondarily, in a pattern recognition and classification application, the unbounded nature of a BP NN causes slow, tedious training with a correspondingly slow decision and computational process when put into practice.
In regard to the clustering of output data into groups, it is known to use “unsupervised” learning with certain types of NNs to produce output clustering of data. Generally speaking in these cases, the NNs is provided with input data but not with target output data. Thus, the NN uses its learning algorithm and connection weighting to group the results it gets into clusters of similar results. This, by itself, does not work well for pattern recognition and classification as the “unsupervised” NN provides its own groupings rather than any that might be desired and pre-determined. Thus, while well-designed NNs can perform complex decision-making from a wide variety of data inputs, the current methods of using a back propagation NN for classifying the occupancy of a vehicle seat are inefficient and often contain hidden computational errors. Furthermore, other NNs employing unsupervised learning can group the resultant outputs but cannot separate the results into any predetermined classifications.
Accordingly, there remains a need in the art for a method of occupant classification for a vehicle seat that employs a NN that is trained by supervised learning to define a predetermined a set of classifications, and that is also capable of processing any available input data and separating the resultant output into predetermined classifications.